<div align="center">
<h1>The Hands-on Reinforcement Learning course 🚀 </h1>
<h2>From zero to HERO 🦸🏻‍🦸🏽</h2>
<h3><i>Out of intense complexities, intense simplicities emerge.</i></h3>
<h4>-- Winston Churchill</h4>
</div>

![](http://datamachines.xyz/wp-content/uploads/2021/11/PHOTO-2021-11-05-13-54-11.jpg)

[![Twitter Follow](https://img.shields.io/twitter/follow/paulabartabajo_?label=Follow&style=social)](https://twitter.com/paulabartabajo_)

## Contents

* [Welcome to the course](#welcome-to-the-course-)
* [Lectures](#lectures)
* [Wanna contribute?](#wanna-contribute)
* [Let's connect!](#lets-connect)

## Welcome to the course 🤗❤️

Welcome to my step by step hands-on-course that will take you from basic reinforcement learning to cutting-edge deep RL.

We will start with a short intro of what RL is, what is it used for, and how does the landscape of current
RL algorithms look like.

Then, in each following chapter we will solve a different problem, with increasing difficulty:
- 🏆 easy
- 🏆🏆 medium
- 🏆🏆🏆  hard

Ultimately, the most complex RL problems involve a mixture of reinforcement learning algorithms, optimizations and Deep Learning techniques.

You do not need to know deep learning (DL) to follow along this course.

I will give you enough context to get you familiar with DL philosophy and understand
how it becomes a crucial ingredient in modern reinforcement learning.

## Lectures

0. [Introduction to Reinforcement Learning](https://datamachines.xyz/2021/11/17/hands-on-reinforcement-learning-course-part-1/)
1. [Q-learning to drive a taxi 🏆](01_taxi/README.md)
2. [SARSA to beat gravity 🏆](02_mountain_car/README.md)
3. [Parametric Q learning to keep the balance 💃 🏆](03_cart_pole/README.md)
4. [Policy gradients to land on the Moon 🏆](04_lunar_lander/README.md)

## Wanna contribute?

There are 2 things you can do to contribute to this course:

1. Spread the word and share it on [Twitter](https://ctt.ac/Aa7dt), [LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=http%3A//datamachines.xyz/the-hands-on-reinforcement-learning-course-page/&title=The%20hands-on%20Reinforcement%20Learning%20course&summary=Wanna%20learn%20Reinforcement%20Learning?%20%F0%9F%A4%94%0A%40paulabartabajo%20has%20a%20course%20on%20%23reinforcementlearning,%20that%20takes%20you%20from%20zero%20to%20PRO%20%F0%9F%A6%B8%F0%9F%8F%BB%E2%80%8D%F0%9F%A6%B8%F0%9F%8F%BD.%0A%0A%F0%9F%91%89%F0%9F%8F%BD%20With%20lots%20of%20Python%0A%F0%9F%91%89%F0%9F%8F%BD%20Intuitions,%20tips%20%26%20tricks%20explained.%0A%F0%9F%91%89%F0%9F%8F%BD%20And%20free,%20by%20the%20way.%0A%0AReady%20to%20start?%20Click%20%F0%9F%91%87%F0%9F%8F%BD%F0%9F%91%87%F0%9F%8F%BE%F0%9F%91%87%F0%9F%8F%BF%0A%0A%23MachineLearning&source=)

2. Open a [pull request](https://github.com/Paulescu/hands-on-rl/pulls) to fix a bug or improve the code readability.

### Thanks ❤️
Special thanks to all the students who contributed with valuable feedback
and pull requests ❤

- [Neria Uzan](https://www.linkedin.com/in/neria-uzan-369803107/)
- [Anthony Lapadula](https://www.linkedin.com/in/anthony-lapadula-9343a5b/)
- [Petar Sekulić](https://www.linkedin.com/in/petar-sekulic-ml/)

## Let's connect!

👉🏽 Subscribe for **FREE** to the [Real-World ML newsletter](https://realworldml.net/subscribe/) 🧠

👉🏽 Follow me on [Twitter](https://twitter.com/paulabartabajo_) and [LinkedIn](https://www.linkedin.com/in/pau-labarta-bajo-4432074b/) 💡
